A two-steps approach to improve the performance of Android malware detectors. (arXiv:2205.08265v1 [cs.CR])

The popularity of Android OS has made it an appealing target to malware
developers. To evade detection, including by ML-based techniques, attackers
invest in creating malware that closely resemble legitimate apps. In this
paper, we propose GUIDED RETRAINING, a supervised representation learning-based
method that boosts the performance of a malware detector. First, the dataset is
split into "easy" and "difficult" samples, where difficulty is associated to
the prediction probabilities yielded by a malware detector: for difficult
samples, the probabilities are …

android android malware malware performance